Highlights: Industry 4.0 (I4.0) technologies and Supply Chain Performance are systematically reviewed and summarized. Content analysis is employed to explore the link between I4.0 technologies and supply chain performance measures. Proposed a framework for supply chain 4.0 Performance Measurement. Recommendations for future research opportunities are identified.
Abstract Companies require a greater understanding of the Supply Chain (SC) benefits that can be gained from industry 4.0 (I4.0) and, more specifically, which technologies and concepts that can improve certain SC performance measures. A state-of-the-art systematic literature review (SLR) has been done on supply chain performance measurement linked with various industry 4.0 technologies. Based on the findings of the review through content analysis, this paper presents a framework for exploring the usage of I4.0 technologies to identify the potential supply chain performance measures. This framework includes the dimensions of Procurement 4.0, Manufacturing 4.0, Logistics 4.0, and Warehousing 4.0. As a scientific contribution, this study has validated the proposed framework through case studies, where the existing studies are limited. Finally, several fruitful future possible extensions have been discussed based on the proposed framework.
Supply Chain 4.0 performance measurement: A systematic literature review, framework development, and empirical evidence
2022-04-21
Article (Journal)
Electronic Resource
English
Supply Chain 4.0 , Industry 4.0 , Supply Chain Performance , Systematic Literature Review , Content analysis , Technologies , Concepts , AI , Artificial Intelligence , AM , Additive Manufacturing , AR , Augmented Reality , BDA , Big Data Analytics , BD , Big Data , CPS , Cyber-Physical System , DL , Deep Learning , DT , Digital Twin , ERP , Enterprise Resource Planning , IIoT , Industrial Internet of Things , IoS , Internet of Services , IoT , Internet of Things , IT , Information Technology , I4.0 , KPI , Key Performance Indicator , MES , Manufacturing Execution Systems , ML , Machine Learning , RFID , Radio Frequency Identification , SC , Supply Chain , SCM , Supply Chain Management , STG , Sanovo Technology Group
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